Full Potential
"Full potential" research explores maximizing the capabilities of various models and algorithms across diverse fields. Current efforts focus on improving model performance in tasks like program repair, multimodal search, and medical image segmentation, often leveraging large language models (LLMs), diffusion models, and graph neural networks. This research is significant because it aims to enhance the efficiency and accuracy of existing technologies, leading to advancements in areas such as software development, AI-assisted content creation, and healthcare diagnostics. The ultimate goal is to unlock the full capabilities of these models for practical applications and scientific discovery.
Papers
Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses
Aysa Xuemo Fan, Ranran Haoran Zhang, Luc Paquette, Rui Zhang
Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey
Soumya Suvra Ghosal, Souradip Chakraborty, Jonas Geiping, Furong Huang, Dinesh Manocha, Amrit Singh Bedi
Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review
Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu
Examining the Potential and Pitfalls of ChatGPT in Science and Engineering Problem-Solving
Karen D. Wang, Eric Burkholder, Carl Wieman, Shima Salehi, Nick Haber
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes
Marlin Berger, Frederik Cloppenburg, Jens Eufinger, Thomas Gries
The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students
Angel Udias, Antonio Alonso-Ayuso, Ignacio Sanchez, Sonia Hernandez, Maria Eugenia Castellanos, Raquel Montes Diez, Emilio Lopez Cano
IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements
Vageesh Saxena, Benjamin Bashpole, Gijs Van Dijck, Gerasimos Spanakis
Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces
Usashi Chatterjee, Amit Gajbhiye, Steven Schockaert
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio, Sofiene Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration
Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Shaojie Tang